{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Learning Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to plot learning curves for classifiers. Learning curves are extremely useful to analyze if a model is suffering from over- or under-fitting (high variance or high bias). The function can be imported via\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import plot_learning_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6RXZ2NoaGhrJjNC9pTPD+XYCiSk5Opm3btgQHB8vODVZWVnz77bc4OzvTtGlT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Rht2rTBwYOlZRF//vlneHh4VPv+MjMzYTAY4Orqyml3dXVFenq62WvS09PNnq/X65GZmQkAmD17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\nEEKIbkgG+kIIIYQQQnRD/wcU9XMcP78HMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d12e5e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_learning_curves\n",
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.data import iris_data\n",
    "from mlxtend.preprocessing import shuffle_arrays_unison\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Loading some example data\n",
    "X, y = iris_data()\n",
    "X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=123)\n",
    "X_train, X_test = X[:100], X[100:]\n",
    "y_train, y_test = y[:100], y[100:]\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, clf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_learning_curves\n",
      "\n",
      "*plot_learning_curves(X_train, y_train, X_test, y_test, clf, train_marker='o', test_marker='^', scoring='misclassification error', suppress_plot=False, print_model=True, style='fivethirtyeight', legend_loc='best')*\n",
      "\n",
      "Plots learning curves of a classifier.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X_train` : array-like, shape = [n_samples, n_features]\n",
      "\n",
      "    Feature matrix of the training dataset.\n",
      "\n",
      "- `y_train` : array-like, shape = [n_samples]\n",
      "\n",
      "    True class labels of the training dataset.\n",
      "\n",
      "- `X_test` : array-like, shape = [n_samples, n_features]\n",
      "\n",
      "    Feature matrix of the test dataset.\n",
      "\n",
      "- `y_test` : array-like, shape = [n_samples]\n",
      "\n",
      "    True class labels of the test dataset.\n",
      "\n",
      "- `clf` : Classifier object. Must have a .predict .fit method.\n",
      "\n",
      "\n",
      "- `train_marker` : str (default: 'o')\n",
      "\n",
      "    Marker for the training set line plot.\n",
      "\n",
      "- `test_marker` : str (default: '^')\n",
      "\n",
      "    Marker for the test set line plot.\n",
      "\n",
      "- `scoring` : str (default: 'misclassification error')\n",
      "\n",
      "    If not 'misclassification error', accepts the following metrics\n",
      "    (from scikit-learn):\n",
      "    {'accuracy', 'average_precision', 'f1_micro', 'f1_macro',\n",
      "    'f1_weighted', 'f1_samples', 'log_loss',\n",
      "    'precision', 'recall', 'roc_auc',\n",
      "    'adjusted_rand_score', 'mean_absolute_error', 'mean_squared_error',\n",
      "    'median_absolute_error', 'r2'}\n",
      "\n",
      "- `suppress_plot=False` : bool (default: False)\n",
      "\n",
      "    Suppress matplotlib plots if True. Recommended\n",
      "    for testing purposes.\n",
      "\n",
      "- `print_model` : bool (default: True)\n",
      "\n",
      "    Print model parameters in plot title if True.\n",
      "\n",
      "- `style` : str (default: 'fivethirtyeight')\n",
      "\n",
      "    Matplotlib style\n",
      "\n",
      "- `legend_loc` : str (default: 'best')\n",
      "\n",
      "    Where to place the plot legend:\n",
      "    {'best', 'upper left', 'upper right', 'lower left', 'lower right'}\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `errors` : (training_error, test_error): tuple of lists\n",
      "\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/plotting/learning_curves/](http://rasbt.github.io/mlxtend/user_guide/plotting/learning_curves/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_learning_curves.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
 ],
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